Latent Dataset Embedding Index for Faster Database Search
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Solution Overview
Problem
The inefficiencies in database searching due to large data volumes, complex relationships, and poorly designed queries lead to slow search times, especially when hardware resources are limited, resulting in strain on database servers.
Innovation Solution
Generating a latent index based on metadata using an embedding algorithm to reduce the volume of data searched and preserve complex relationships, along with creating an embedded request compatible with the latent index for user-friendly search terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the database contains a significant amount of data, then the data storage capacity is improved, but the search operation time increases
Solution Approach 1:
The patent divides the large database into multiple clusters, where each cluster contains a subset of the data. Search operations are performed on individual clusters rather than the entire database, significantly reducing the search space and operation time while maintaining the ability to handle large volumes of data.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing cluster assignments and metadata for each data point before search operations. This includes creating cluster indexes and organizing data structures in advance, so that during search operations, the system can quickly locate relevant clusters without processing the entire database.
2Reliability
If complex relationships are preserved in the database, then the data integrity is improved, but the query execution time increases
Solution Approach 1:
The patent extracts relationship information from the main database and stores it separately in cluster metadata and indexes. This allows the system to preserve complex relationships for data integrity while avoiding the need to traverse entire relationship graphs during search operations, as the cluster structure pre-organizes the relationship information.
3Productivity
If proper indexing is implemented, then the search efficiency is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a new dimensional organization by creating cluster-based indexes that group data points into hierarchical structures. This adds a cluster dimension to the traditional flat indexing approach, allowing searches to first locate relevant clusters and then search within those clusters, improving efficiency without requiring complex multi-dimensional index structures.
Data Source
AI summary
Methods and systems for scalable dataset content embedding for improved searchability. For example, the system may retrieve a first dataset from a first data source. The system may generate a first data profile of the first dataset. The system may generate a latent index of the first data profile based on processing the first data profile using a first embedding algorithm. The system may receive, via a user interface, a first request for a first text string. The system may generate an embedded request corresponding to the first request based on processing the first text string using the first embedding algorithm. The system may process the embedded request using the latent index. The system may generate for display, in the user interface, a result based on processing the embedded request using the latent index.